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stat.ME2025
Tuning-Free Sampling via Optimization on the Space of Probability Measures
Louis Sharrock, Christopher Nemeth
We introduce adaptive, tuning-free step size schedules for gradient-based sampling algorithms obtained as time-discretizations of Wasserstein gradient flows. The result is a suite…
stat.ME2024
Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flows
Alberto Cabezas, Louis Sharrock, Christopher Nemeth
Continuous normalizing flows (CNFs) learn the probability path between a reference distribution and a target distribution by modeling the vector field generating said path using ne…
stat.ME2024
Control Variate-based Stochastic Sampling from the Probability Simplex
Francesco Barile, Christopher Nemeth
This paper presents a control variate-based Markov chain Monte Carlo algorithm for efficient sampling from the probability simplex, with a focus on applications in large-scale Baye…